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A random split of the dataset into a derivation and a validation dataset was used.
The split of the dataset and the independent test is repeated for 10 times.
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By providing relevant splits of the dataset for each application and inspecting the consistency of associated labels, we form a comparison basis for visual analysis of maritime vessels.
The variation of the prediction performance, which is the result of choosing different splits of the dataset in V-fold cross-validation, needs to be taken into account when selecting and assessing classification and regression models.
The tests results have ruled out any skewness, biasness or variance in the results due to random splitting of the dataset.
We then used SVMs to evaluate the performance of selected variables in 100 classification experiments using random training/test splits of the dataset.
used a random split of the entire dataset to form one derivation dataset (70%) and one validation dataset (30%).
Figure 11 show the confusion matrices for the STIP, STIP+BSIF-TOP, iDT, and iDT+BSIF-TOP features respectively, based on the first split of the HMDB dataset (both HMDB-LQ and HMDB-MQ included).
As a quick test of significance we split each of the datasets into six random sub-groups and confirmed that the visual differences remained qualitatively unchanged among them.
In both experiments, we considered 20 different random splits of the whole dataset into a training set (70% of the whole dataset) and test set (remaining 30% of the whole data set).
Author's response: Firstly, we are not sure about the possibility of splitting the dataset in two groups because of the low number of sequences in each of them (nine).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com